Paper ID #27849Transition Zone: a Training Ethos Designed to Scaffold a Ph.D. SegreeDr. Carmen Torres-S´anchez, Loughborough University Dr Torres-S´anchez is an Associate Professor at Loughborough University, England, United Kingdom, and the Executive Director of the Centre of Doctoral Training in Embedded Intelligence (CDT-EI). She is the architect of the novel Doctoral Transition ZoneTM Training ethos. She has been working in industry- informed, academically-led education for more than 10 years. Her research interests are in the design and manufacture of multifunctional materials with tailored properties to meet
provides adedicated and safe environment for PhDs and post-docs to discuss their career possibilities andplans outside academia.” And lastly, participants liked having a structure for career exploration(i.e., step-by-step process, dedicated time). One participant said “Good to do it from start to end -it becomes a journey where all the small steps create a bigger picture.”When examining the responses to “What did you not like about the program” from all threecohorts, two themes emerged. First, some participants disliked the instructional strategy of smalland large group discussions/activities because it made classes long and exhausting. We suspectparticipants, who are upper PhDs and post-doctoral fellows, may not have taken a class in acouple
graduateengineering students’ attitudes and perceptions about writing and the writing process, the surveyitself is quite long, averaging participants over 30 minutes to complete. Most interesting weregraduate engineering student responses to two of the surveys given, which will be discussed atlength later and are described in our prior work. Literature suggests that survey fidelity decreases with longer surveys, due to “surveyfatigue” [24] in which participants lose focus or care over their answers, an unwelcomephenomenon in the collection of data. Therefore, the purpose of this paper is to present a shortform of the survey which consists of only the survey items that most highly predict writingattitudes. The next section will introduce the two